Regional PV power prediction for improved grid integration

By E. Lorenz, T. Scheidsteger, Johannes Hurka, D. Heinemann, C. Kurz. 2011

304 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/dd3dd50ec917fc80f969ed19ba551680c0efa56e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. F. J. Martínez-de-Pisón
2. Fernando Antoñanzas-Torres
3. Javier Antoñanzas
4. R. Escobar
5. Rubén Urraca
6. N. Osório

Citations in the corpus

• Review of photovoltaic power forecasting (2016, methodology): “The rest used exogenous data, either from NWP (Alessandrini et al., 2015; Dolara et al., 2015a; Lorenz et al., 2007, 2008, 2009, 2011b,a; Zamo et al., 2014b; De Felice et al., 2015; Sperati et al., 2016), meteorological records (Oudjana et al., 2013; De Georgi et al., 2015; Long et al., 2014) or…”